William Tepe
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View article: Advances in developing deep neural networks for finding primary vertices in proton-proton collisions at the LHC
Advances in developing deep neural networks for finding primary vertices in proton-proton collisions at the LHC Open
We are studying the use of deep neural networks (DNNs) to identify and locate primary vertices (PVs) in proton-proton collisions at the LHC. Earlier work focused on finding primary vertices in simulated LHCb data using a hybrid approach th…
View article: Advances in developing deep neural networks for finding primary vertices in proton-proton collisions at the LHC
Advances in developing deep neural networks for finding primary vertices in proton-proton collisions at the LHC Open
We are studying the use of deep neural networks (DNNs) to identify and locate primary vertices (PVs) in proton-proton collisions at the LHC. Earlier work focused on finding primary vertices in simulated LHCb data using a hybrid approach th…
View article: Comparing and improving hybrid deep learning algorithms for identifying and locating primary vertices
Comparing and improving hybrid deep learning algorithms for identifying and locating primary vertices Open
Using deep neural networks to identify and locate proton-proton collision points, or primary vertices, in LHCb has been studied for several years. Preliminary results demonstrated the ability for a hybrid deep learning algorithm to achieve…
View article: Progress in developing a hybrid deep learning algorithm for identifying and locating primary vertices
Progress in developing a hybrid deep learning algorithm for identifying and locating primary vertices Open
The locations of proton-proton collision points in LHC experiments are called primary vertices (PVs). Preliminary results of a hybrid deep learning algorithm for identifying and locating these, targeting the Run 3 incarnation of LHCb, have…